Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification
Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification
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DOI：
10.1016/j.patcog.2015.12.017
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Yong Xu;Zheng Zhang;Guangming Lu;Jian Yang
中科院分区：
文献类型：
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作者：
Yong Xu;Zheng Zhang;Guangming Lu;Jian Yang
虽然大多数人脸是轴对称的对象，但现实世界中的人脸图像很少是轴对称的图像。在过去的几年里，关于人脸识别的研究很多，但对这一问题的关注很少，探索和利用人脸的轴对称特性进行人脸识别的研究也很少。本文考虑到人脸的轴对称特性，设计了一种生成近轴对称虚拟词典的框架，以提高人脸识别的准确率。值得注意的是，这种生成轴对称虚拟人脸图像的新算法在数学上非常容易处理，并且很容易实现。大量的实验结果表明，该方法得到的虚拟人脸图像比原始人脸图像具有更好的人脸识别效果。此外，在不同数据库上的实验结果也表明，与现有的图像预处理算法相比，该方法能够达到令人满意的分类精度。提出的方法的matlab代码可以在http://www.yongxu.org/lunwen.html.上获得
Though most of the faces are axis-symmetrical objects, few real-world face images are axis-symmetrical images. In the past years, there are many studies on face recognition, but only little attention is paid to this issue and few studies to explore and exploit the axis-symmetrical property of faces for face recognition are conducted. In this paper, we take the axis-symmetrical nature of faces into consideration and design a framework to produce approximately axis-symmetrical virtual dictionary for enhancing the accuracy of face recognition. It is noteworthy that the novel algorithm to produce axis-symmetrically virtual face images is mathematically very tractable and quite easy to implement. Extensive experimental results demonstrate the superiority in face recognition of the virtual face images obtained using our method to the original face images. Moreover, experimental results on different databases also show that the proposed method can achieve satisfactory classification accuracy in comparison with state-of-the-art image preprocessing algorithms. The MATLAB code of the proposed method can be available at http://www.yongxu.org/lunwen.html.